It is known that the “appearance” of an object is determined by a number of components including a specular reflection component, which is a part of incoming light that has been reflected from the object and then sensed as a sort of “gloss”, and a diffuse reflection component, which is sensed after the incoming light has been scattered a number of times inside the object. That is to say, every image of an object that we view daily is normally the sum of various components of the light that strike our eyes after having gone through multiple different courses from the object. And examples of those components include the “specular reflection component” and the “diffuse reflection component”. That is why as for a huge number of pixels that form an object's image, their light intensities may be graded according to their “specular reflection components” and “diffuse reflection components”. Specifically, pixels, of which the specular reflection component dominates, may be treated as pixels that form a “specular reflection region”, while pixels, of which the diffuse reflection component dominates, may be treated as pixels that form a “diffuse reflection region”.
Recently, in order to compress given information and archive it digitally or to obtain information about the shape and surface property of an object, an image component separating technique, by which a given image is separated into those various components (and which will be simply referred to herein as an “image separation technique”), has been adopted more and more often (see Non-Patent Documents Nos. 1, 2 and 3). Such an image separation is done more and more frequently because processing could be carried out more accurately on a component by component basis, compared to a situation where the entire image is subjected to the same processing collectively. For example, if someone needs to know what impact reflection of incoming light from an object's surface will have, it is very important to separate the incoming light into a component of the light that is being transmitted from inside of the object and a component of the light that is being reflected from the object's surface. And a technique for estimating the ratio of those components in a given image and separating the image into them on a pixel-by-pixel basis is the image component separating technique (or simply an “image separation technique”).
As a technique using such image separation, a so-called “model based image processing” technique for representing those separated image components on their own models is well known in the art.
In particular, a “model based image synthesis” is a technique that is used extensively in the field of computer graphics (CG). According to that technique, the appearance of a given object is modeled as a function of the respective locations of a viewpoint and a light source and a normal to the object, thereby performing various kinds of image processing to obtain a viewpoint changed image, which represents how the same object would look when viewed from a different viewpoint from the one for shooting, or a light source environment changed image, which represents how the same object would look when irradiated with a light source at a different location from the one for shooting. In addition, since an image can be represented using only model parameters, data can be compressed efficiently, too. For example, a technique for separating a given image into a specular reflection component and a diffuse reflection component and modeling those specular and diffuse reflection components after the Cook-Torrance model and the Lambertian model, respectively, is known.
Various techniques for separating a given image into a specular reflection component and a diffuse reflection component have been known so far. Among other things, techniques that use polarization information and techniques that use color information have been researched extensively these days because the given image can be separated into specular and diffuse reflection components with a simple device according to such techniques.
According to the separation technique using the polarization information, linearly polarized light is projected toward an object and a polarization filter, which is arranged between a camera and the object, is rotated within a plane that intersects with the optical axis at right angles, thereby separating the light reflected from the object into a specular reflection component and a diffuse reflection component (see Patent Document No. 1, for example).
It is known that if the angle of rotation of a linear polarization filter (which is also called a “polarization principal axis angle” that is an angle defining the polarization principal axis direction) is identified by φ, then the light intensity at each of the pixels of an image viewed varies like a sin function with respect to the angle of rotation φ. If the amplitude component and bias component of that sin function are used as a “specular reflection component” and a “diffuse reflection component”, respectively, the image can be separated. That is to say, the specular and diffuse reflection components can be obtained quantitatively for each of multiple pixels that form the image.
According to such a technique, however, in a region where the angle of emittance is rather great (near the occluding edge), a specular reflection component that should not be present there would be detected anyway as will be described later. That problem prevents the specular/diffuse reflection component separating technique using the polarization information from achieving sufficiently high accuracy.
Meanwhile, a technique using color information is known widely as a dichroic reflection model (see Non-Patent Document No. 4, for example). This is a technique for separating light into a specular reflection component and a diffuse reflection component by paying attention to the fact that the color vector of a specular reflection component is observed as a light source color vector but the color vector of a diffuse reflection component is observed as an object color vector. In general, the light source color vector is already known because the color is often white or observed by another technique. According to that technique, however, unless the object color vector of the object is known, the light cannot be separated into specular and diffuse reflection components unambiguously. For that reason, such a technique cannot be used to separate light into a specular reflection component and a diffuse reflection component accurately.
And it is known that such inaccurate separation into specular and diffuse reflection components should be a grave issue when a model-based image synthesis, which is adopted in the fields of digital archiving and augmented reality, needs to be carried out.
Citation List
Patent Literature
                Patent Document No. 1: Japanese Patent Application Laid-Open Publication No. 11-41514Non-Patent Literature        Non-Patent Document No. 1: Y. Sato, M. D. Wheeler, and K. Ikeuchi, “Object Shape and Reflectance Modeling from Observation”, SIGGRAPH 97, pp. 379-387, 1997        Non-Patent Document No. 2: Daisuke Miyazaki, Takushi Shibata and Katsushi Ikeuchi, “Wavelet-Texture Method: BTF Compression Using Daubechies Wavelet, Parametric Reflection Model and Circular Polarizer”, Transaction of the Institute of Electronics, Information and Communication Engineers, Vol. J90-D, No. 8, pp. 2081-2093, 2007        Non-Patent Document No. 3: T. Shibata, T. Takahashi, D. Miyazaki, Y. Sato, and K. Ikeuchi, “Creating Photorealistic Virtual Model with Polarization Based Vision System”, in Proceedings of SPIE (Polarization Science and Remote Sensing II, Part of SPIE's International Symposium on Optics and Photonics 2005), Vol. 5888, pp. 25-35, 2005        Non-Patent Document No. 4: S. K. Nayar, X. S. Fang, and T. Boult, “Separation of Reflection Components Using Color and Polarization”, International Journal of Computer Vision, Vol. 21, Iss. 3, pp. 163-186, 1997        